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          <section class="tex2jax_ignore mathjax_ignore" id="frequently-asked-questions">
<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">¶</a></h1>
<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-03' } *-->
<!-- [TOC] placeholder - DO NOT REMOVE -->
<p>Looking for help? Submit bugs, ask questions, suggest content, and request
features on our
<a class="reference external" href="https://github.com/pair-code/lit/issues/">Github issues list</a>.</p>
<section id="model-and-data-types">
<h2>Model and Data Types<a class="headerlink" href="#model-and-data-types" title="Link to this heading">¶</a></h2>
<p>LIT can handle a variety of models with different input and output types, and
works with any modern ML framework. For more information, see
<a class="reference internal" href="components.html#framework-and-model-support"><span class="std std-ref">Framework &amp; Model Support</span></a>.</p>
<p>In addition to text, LIT has good support for different modalities, including
images and tabular data. For examples, see:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">Tabular demo</a> -
multi-class classification on tabular (numeric and categorical string) data,
using the
<a class="reference external" href="https://www.tensorflow.org/datasets/catalog/penguins">Palmer Penguins</a>
dataset.</p></li>
</ul>
<p>For more details, see
<a class="reference internal" href="api.html#type-system"><span class="std std-ref">the features guide to input and output types</span></a>.</p>
</section>
<section id="languages">
<h2>Languages<a class="headerlink" href="#languages" title="Link to this heading">¶</a></h2>
<p>All strings in LIT are unicode and most components use model-provided
tokenization if available, so in most cases non-English languages and non-Latin
scripts should work without any modifications.</p>
</section>
<section id="scale">
<h2>Scale<a class="headerlink" href="#scale" title="Link to this heading">¶</a></h2>
<section id="dataset-size">
<h3>Dataset Size<a class="headerlink" href="#dataset-size" title="Link to this heading">¶</a></h3>
<p>LIT can comfortably handle 10k-100k datapoints, depending on the speed of the
server (for hosting the model) and your local machine (for viewing the UI). When
working with large datasets, there are a couple caveats:</p>
<ul class="simple">
<li><p>LIT expects predictions to be available on the whole dataset when the UI
loads. This can take a while if you have a lot of examples or a larger model
like BERT. In this case, we recommend adding the flag <code class="docutils literal notranslate"><span class="pre">--warm_start=1</span></code> (or
pass <code class="docutils literal notranslate"><span class="pre">warm_start=1</span></code> to the <code class="docutils literal notranslate"><span class="pre">Server</span></code> constructor in Python) to pre-compute
predictions before starting the server.</p></li>
<li><p>Datasets containing images may take a while to load. If full “native”
resolution is not needed (such as if the model operates on a smaller size
anyway, such as 256x256), then you can speed things up by resizing images in
your <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> loading code.</p></li>
<li><p>LIT uses WebGL for the embedding projector (via
<a class="reference external" href="https://github.com/PAIR-code/scatter-gl">ScatterGL</a>) and for the Scalars
and Dive modules (via <a class="reference external" href="https://github.com/PAIR-code/megaplot">Megaplot</a>),
which may be slow on older machines if you have more than a few thousand
points.</p></li>
</ul>
<p>If you have more data, you can use <code class="docutils literal notranslate"><span class="pre">Dataset.sample</span></code> or <code class="docutils literal notranslate"><span class="pre">Dataset.slice</span></code> to select
a smaller number of examples to load. You can also pass individual examples to
LIT <a class="reference internal" href="#sending-examples-from-another-tool">through URL params</a>, or load custom
data files at runtime using the settings (⚙️) menu.</p>
</section>
<section id="large-models">
<h3>Large Models<a class="headerlink" href="#large-models" title="Link to this heading">¶</a></h3>
<p>LIT can work with large or slow models, as long as you can wrap them into the
model API. If you have more than a few preloaded datapoints, however, you’ll
probably want to use <code class="docutils literal notranslate"><span class="pre">warm_start=1</span></code> (or pass <code class="docutils literal notranslate"><span class="pre">--warm_start=1</span></code> as a flag) to
pre-compute predictions when the server loads, so you don’t have to wait when
you first visit the UI.</p>
<p>Also, beware of memory usage: since LIT keeps the models in memory to support
new queries, only so many models can fit on a single node or GPU. If you want to
load more or larger models than can fit in local memory, you can host your model
with your favorite serving framework and connect to it using a custom
<a class="reference internal" href="api.html#models"><span class="std std-ref"><code class="docutils literal notranslate"><span class="pre">Model</span></code></span></a> class.</p>
<p>We also have experimental support for using LIT as a lightweight model server;
this can be useful, e.g., for comparing an experimental model running locally
against a production model already running in an existing LIT demo. See
<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/remote_model.py"><code class="docutils literal notranslate"><span class="pre">remote_model.py</span></code></a>
for more details.</p>
</section>
</section>
<section id="privacy-and-security">
<h2>Privacy and Security<a class="headerlink" href="#privacy-and-security" title="Link to this heading">¶</a></h2>
<p>LIT allows users to query the model, as well as to view the loaded evaluation
data. The LIT UI state is ephemeral and exists only in the browser window;
however, model predictions and any newly-generated examples (including as
manually entered in the web UI) are stored in server memory, and if <code class="docutils literal notranslate"><span class="pre">--data_dir</span></code>
is specified, may be cached to disk.</p>
<p>LIT has the ability to create or edit datapoints in the UI and then save them to
disk. If you do not want the tool to be able to write edited datapoints to disk,
then pass the <code class="docutils literal notranslate"><span class="pre">--demo_mode</span></code> runtime flag to the LIT server.</p>
<section id="i-have-proprietary-data-is-lit-secure-for-my-team-to-use">
<h3>I have proprietary data. Is LIT secure for my team to use?<a class="headerlink" href="#i-have-proprietary-data-is-lit-secure-for-my-team-to-use" title="Link to this heading">¶</a></h3>
<p>We don’t store, collect or share datasets, models or any other information
loaded into LIT. When you run a LIT server, anyone with access to the web
address of the server will be able to see data from the loaded datasets and
interact with the loaded models. If you need to restrict access to a LIT
server, then make sure to configure the hosting of your LIT server to do so.</p>
<p>The default LIT development server does not implement any explicit access
controls. However, this server is just a thin convenience wrapper, and the
underlying WSGI App can be easily exported and used with additional middleware
layers or external serving frameworks. See
<a class="reference internal" href="docker.html"><span class="std std-doc">Running LIT in a Docker container</span></a> for an example.</p>
</section>
</section>
<section id="workflow-and-integrations">
<h2>Workflow and Integrations<a class="headerlink" href="#workflow-and-integrations" title="Link to this heading">¶</a></h2>
<section id="sending-examples-from-another-tool">
<h3>Sending examples from another tool<a class="headerlink" href="#sending-examples-from-another-tool" title="Link to this heading">¶</a></h3>
<p>LIT can read input fields directly from the URL; they should be encoded as
<code class="docutils literal notranslate"><span class="pre">data_&lt;fieldname&gt;=&lt;value&gt;</span></code>, and field names should match those in the (default)
dataset.</p>
<p>There is also (experimental) support for sending more than one example, as long
as the total URL length is within the browser’s size limit. Specify as above,
but using <code class="docutils literal notranslate"><span class="pre">data0</span></code>, <code class="docutils literal notranslate"><span class="pre">data1</span></code>, <code class="docutils literal notranslate"><span class="pre">data2</span></code>, e.g. <code class="docutils literal notranslate"><span class="pre">data0_&lt;fieldname&gt;=&lt;value&gt;</span></code>.</p>
</section>
<section id="downloading-or-exporting-data">
<h3>Downloading or exporting data<a class="headerlink" href="#downloading-or-exporting-data" title="Link to this heading">¶</a></h3>
<p>Currently, there are three ways to export data from the LIT UI:</p>
<ul class="simple">
<li><p>In the Data Table, you can copy or download the current view in CSV format -
see <a class="reference internal" href="ui_guide.html#data-table"><span class="std std-ref">the UI guide</span></a> for more details.</p></li>
<li><p>In the “Dataset” tab of the settings (⚙️) menu, you can enter a path to save
data to. Data is pushed to the server and written by the server backend, so
be sure the path is writable.</p></li>
<li><p>If using LIT in a Colab or other notebook environment, you can access the
current selection from another cell using <code class="docutils literal notranslate"><span class="pre">widget.ui_state.primary</span></code>,
<code class="docutils literal notranslate"><span class="pre">widget.ui_state.selection</span></code>, and <code class="docutils literal notranslate"><span class="pre">widget.ui_state.pinned</span></code>.</p></li>
</ul>
</section>
<section id="loading-data-from-the-ui">
<h3>Loading data from the UI<a class="headerlink" href="#loading-data-from-the-ui" title="Link to this heading">¶</a></h3>
<p>There is limited support for this via the settings (⚙️) menu. Select a dataset,
and enter a path to load from:</p>
<p><img alt="Load data from the UI" src="_images/lit-load-data.png" /></p>
<p>The supported format(s) depend on the dataset class; in most cases, the user
should implement the <code class="docutils literal notranslate"><span class="pre">load()</span></code> method on their dataset class to handle the
appropriate format.</p>
</section>
<section id="using-components-outside-the-lit-ui">
<h3>Using components outside the LIT UI<a class="headerlink" href="#using-components-outside-the-lit-ui" title="Link to this heading">¶</a></h3>
<p>Python components such as models, datasets, and generators are designed to
support standalone use. These don’t depend on the LIT serving framework, and you
can treat them as any other Python class and use from Colab, regular scripts,
bulk inference pipelines, etc. For an example, see
<a class="reference internal" href="api.html#using-lit-components-outside-of-lit"><span class="std std-ref">the API documentation</span></a>.</p>
<p>For the frontend, it’s a little more difficult. In order to respond to and
interact with the shared UI state, there’s a lot more “framework” code involved
(see the <a class="reference internal" href="frontend_development.html"><span class="std std-doc">frontend development guide</span></a> for more).
We’re working on refactoring the LIT
<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/modules"><code class="docutils literal notranslate"><span class="pre">modules</span></code></a> to separate
framework and API code from the visualizations (e.g.
<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/elements"><code class="docutils literal notranslate"><span class="pre">elements</span></code></a>), which can
then be re-used in other environments.</p>
</section>
<section id="training-models-with-lit">
<h3>Training models with LIT<a class="headerlink" href="#training-models-with-lit" title="Link to this heading">¶</a></h3>
<p>LIT is primarily an evaluation/inference-time tool, so we don’t provide any
official training APIs. However, to facilitate code reuse you can easily add
training methods to your model class. In fact, several of our demos do exactly
this, using LIT’s <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> objects to manage training data along with standard
training APIs (such as Keras’ <code class="docutils literal notranslate"><span class="pre">model.fit()</span></code>). See
<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py"><code class="docutils literal notranslate"><span class="pre">glue/models.py</span></code></a>
for examples.</p>
</section>
</section>
</section>

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<li><a class="reference internal" href="#">Frequently Asked Questions</a><ul>
<li><a class="reference internal" href="#model-and-data-types">Model and Data Types</a></li>
<li><a class="reference internal" href="#languages">Languages</a></li>
<li><a class="reference internal" href="#scale">Scale</a><ul>
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<li><a class="reference internal" href="#sending-examples-from-another-tool">Sending examples from another tool</a></li>
<li><a class="reference internal" href="#downloading-or-exporting-data">Downloading or exporting data</a></li>
<li><a class="reference internal" href="#loading-data-from-the-ui">Loading data from the UI</a></li>
<li><a class="reference internal" href="#using-components-outside-the-lit-ui">Using components outside the LIT UI</a></li>
<li><a class="reference internal" href="#training-models-with-lit">Training models with LIT</a></li>
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